H-Index
79
Scimago Lab
powered by Scopus
JCR
Clarivate
Analytics
12%
Acceptance
Rate
call: +1.631.470.9640
Mon-Fri 10 am - 2 pm EST

Logo

Medical Science Monitor Basic Research
AmJCaseRep

Annals
ISI-Home

eISSN: 1643-3750

Performance of Diagnostic Model for Differentiating Between COVID-19 and Influenza: A 2-Center Retrospective Study

Jingwen Li, Simin Li, Xiaoming Qiu, Wenyan Zhu, Linfeng Li, Bo Qin

Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland)

Med Sci Monit 2021; 27:e932361

DOI: 10.12659/MSM.932361

Available online: 2021-05-05

Published: 2021-05-12


#932361

BACKGROUND: COVID-19 and influenza share many similarities, such as mode of transmission and clinical symptoms. Failure to distinguish the 2 diseases may increase the risk of transmission. A fast and convenient differential diagnosis between COVID-19 and influenza has significant clinical value, especially for low- and middle-income countries with a shortage of nucleic acid detection kits. We aimed to establish a diagnostic model to differentiate COVID-19 and influenza based on clinical data.
MATERIAL AND METHODS: A total of 493 patients were enrolled in the study, including 282 with COVID-19 and 211 with influenza. All data were collected and reviewed retrospectively. The clinical and laboratory characteristics of all patients were analyzed and compared. We then randomly divided all patients into development sets and validation sets to establish a diagnostic model using multivariate logistic regression analysis. Finally, we validated the diagnostic model using the validation set.
RESULTS: We preliminarily established a diagnostic model for differentiating COVID-19 from influenza that consisted of 5 variables: age, dry cough, fever, white cell count, and D-dimer. The model showed good performance for differential diagnosis.
CONCLUSIONS: This initial model including clinical features and laboratory indices effectively differentiated COVID-19 from influenza. Patients with a high score were at a high risk of having COVID-19, while patients with a low score were at a high risk of having influenza. This model could help clinicians quickly identify and isolate cases in the absence of nucleic acid tests, especially during the cocirculation of COVID-19 and influenza. Owing to the study’s retrospective nature, further prospective study is needed to validate the accuracy of the model.

Keywords: COVID-19, Influenza, Human, Models, Statistical



Back